Modeling

We are first making a steady-state model, using the SimBiology software on MATLAB. The steady-state scenario is considered in order to find when the production rate of the green fluorescent protein would match its dilution and degradation rates. The final steady-state model would show two plots demonstrating the response of the system to an increase of NO in the negative signal and an increase of adenosine in the positive signal. We are then planning to make a dynamic model by breaking down the process to its basic reactions and kinetic laws, so we could find the concentration of any species at any time. The dynamic model should in the end show plots of different species’ concentrations as a function of time. We plan to use the dynamic model to carry out more analysis on how different parameters change the output or how sensitive the output is with respect to any given parameter. We could use this analysis to optimise the overall process by increasing certain values and repressing others (eg. we could alter the number of base pairs on the sRNA in order to control its binding affinity to fRNA). Next by the end of September, we plan to have a working program, where we could input concentrations of certain molecules such as IL-10 and model the response of the system by means of time. The program should also determine the amount of overshoot a specific number of e-coli could have, in order to to retain a healthy concentration of IL-10. However all this modelling would be based on specific assumptions, and would not accurately recreate the real world where random parameters are involved in the process. Therefore we would carry out some stochastic analysis on all the three previously created models, using statistics to give a better understanding of how the model would function in the real world.